In this work, we propose a novel generatively trained regularizer utilizing a global receptive field that yields high-quality reconstructions even in case of strong noise or heavily undersampled measurements.
In Fig. REF , we show how our model is able to satisfactorily reconstruct ct images from noisy (i.e. low tube current) and incomplete (i.e. limited-angle or few-view) data without observable artifacts.
In fact, using this regularizer we can synthesize naturally appearing ct images without any data (see Fig. REF ).
In contrast to feed-forward formulations [1]}, [2]}, we cast the reconstruction as a variational problem.
This helps interpretability of the trained regularizer by means of analyzing its induced distribution as well as the posterior distribution of any type of reconstruction problem.
We apply a trained model to limited-angle and few-view reconstruction problems, and compare our approach quantitatively and qualitatively with traditional reconstruction algorithms.
In addition, we perform experiments which leverage the probabilistic nature of our approach, such as prior and posterior sampling.
